136 citations · 150 across the 4 of their papers we have counts for
8 papers · 1 filter
Unsupervised Extractive Summarization using Pointwise Mutual Information
Vishakh Padmakumar, He He
Unsupervised approaches to extractive summarization usually rely on a notion of sentence importance defined by the semantic similarity between a sentence and the document. We propo…
Text Generation by Learning from Demonstrations
Richard Yuanzhe Pang, He He
Current approaches to text generation largely rely on autoregressive models and maximum likelihood estimation. This paradigm leads to (i) diverse but low-quality samples due to mis…
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models
Lifu Tu, Garima Lalwani, Spandana Gella +1
Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to…
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization
Esin Durmus, He He, Mona Diab
Neural abstractive summarization models are prone to generate content inconsistent with the source document, i.e. unfaithful. Existing automatic metrics do not capture such mistake…
Decoupling Strategy and Generation in Negotiation Dialogues
He He, Derek Chen, Anusha Balakrishnan +1
We consider negotiation settings in which two agents use natural language to bargain on goods. Agents need to decide on both high-level strategy (e.g., proposing $50) and the exec…
QuAC : Question Answering in Context
Eunsol Choi, He He, Mohit Iyyer +5
We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1)…